Although you probably haven’t heard much about the 26-year-old single mother in the hi-tech news, Brenda is one of the intelligences behind artificial intelligence.

For $9 a day, Brenda, who lives in the slum district Kibera in Nairobi, helps code information for self-driving cars, along with a thousand co-workers, for San Francisco-based Samasource (founded 2008):

In her eight-hour shift, she creates training data. Information – images, most often – prepared in a way that computers can understand.

Brenda loads up an image, and then uses the mouse to trace around just about everything. People, cars, road signs, lane markings – even the sky, specifying whether it’s cloudy or bright. Ingesting millions of these images into an artificial intelligence system means a self-driving car, to use one example, can begin to “recognise” those objects in the real world. The more data, the supposedly smarter the machine. Dave Lee, “Why Big Tech pays poor Kenyans to teach self-driving cars” at BBC

Samasource, one of whose purposes is to provide jobs for disadvantaged people, provides data to firms like Google, Microsoft, and Yahoo. In urban Kenya, as in much of Africa, digital literacy is high but has largely bypassed the PC era, primarily through the use of cell phones. Although $9 a day seems very low pay, it is a step up from the $2 a day which is common in Brenda’s subsistence economy surroundings. High-tech industry work is often a stepping stone to better jobs as well.

Samasource is not, of course, the only company hiring large numbers of workers to develop automated systems. Scale, started in 2016, uses 10,000 contractors, mainly students and moms working from home, not only for self-driving vehicle projects but also to develop automated systems for search and recommendation:

Airbnb, for example, is looking for more ways of being able to ascertain what kinds of homes repeat customers like and don’t like, and also to start to provide other ways of discovering places to stay that are based not just on location and number of bedrooms (which becomes more important especially in cities where you may have too many choices and want a selection more focused on what you are more likely to rent). Ingrid Lunden, “Scale, whose army of humans annotate raw data to train self-driving and other AI systems, nabs $18M” at TechCrunch

Founders Alexandr Wang and Lucy Guo see their company as offering “more interaction and nuanced responses than the typical microtask asked of a Turker” at Amazon’s Mechanical Turk, an earlier model of the same basic idea.

Engineer.ai’s “Builder” product breaks projects into small “building blocks” of re-usable features that are customized by human engineers all over the world, making the process cheaper than the average process.

Sachin Dev Duggal, founder, said in a statement: “We created Engineer.ai so that everyone can build an idea without learning to code. This investment round validates our approach of making bespoke software effortless. The capital comes at a time of rapid growth and will propel the platform into the mainstream, allowing Builder to open the door for entire categories of companies that could not consider it before.” Mike Butcher, “Engineer.ai raises $29.5M Series A for its AI+Humans software building platform” at TechCrunch

We often hear anxiety in media about artificial intelligence taking away jobs. The jobs that AI (it’s really machine learning) takes do not seem to require much creativity. A person seeking work that does require creativity may hope for a promising employment market in which they will probably be assisted by AI.

Mind Matters features original news and analysis at the intersection of artificial and natural intelligence. Through articles and podcasts, it explores issues, challenges, and controversies relating to human and artificial intelligence from a perspective that values the unique capabilities of human beings. Mind Matters is published by the Walter Bradley Center for Natural and Artificial Intelligence.